This is a request for 5 years of funding through the "Mentored Quantitative Research Career Award" (K25) mechanism. The applicant, a biomedical/electrical engineer, proposes a comprehensive training/research program in drug abuse and brain imaging. The long term goal of the applicant is to become an independent and interdisciplinary investigator, skilled in the application of functional magnetic resonance imaging (fMRI) methods for the study of substance abuse research. The research component of the award will develop and improve fMRI techniques for the study of substance abuse and addiction in human subjects. Current fMRI technology suffers from three shortcomings which limit its usefulness in a drug-abuse context. First, stimuli for craving studies are limited to auditory and visual modalities, while odors are known to cause powerful emotional effects. Second, motion artifacts remain a serious problem in fMRI studies, especially for drug-dependent populations. No independent verification of motion currently exists, and motion compensation software is not reliable. Third, slowly varying physiological "noise" comprises the major signal component of fMRI data sets, often swamping the effect being measured. Current data analysis methods attempt to exclude this noise from analysis, but the noise remains a critical limitation of all current fMRI data analysis paradigms. Three separate technical development efforts are proposed. First, an odor delivery system, compatible with the high magnetic fields in the fMRI scanner, will be developed and reduced to practice. This system will enable fMRI studies of craving induced by odor, known to be a potent modality. Second, a motion analysis system will be used to obtain accurate measurements of subject motion during scans. After validation, software phantoms will be developed, and together with the motion analysis system will evaluate available motion analysis packages. Finally, motion data will be incorporated into scanner function, so that resulting fMRI data will be almost free of motion artifacts. Third, a wavelet-based fractal analysis of fMRI data will be developed. This method focuses on the slowly varying physiological "noise" which plagues other analysis techniques, turning a liability into an asset and our work shows that it readily highlights drug effects. The training plan will include coursework in neuroscience, pharmacology, and drug abuse; seminars in responsible conduct of research; laboratory rotations; grant writing experience; and regular meetings with the mentors. The timeline of the training will be matched to coincide with the progress of the research projects.